Image: A hamburger, earlier today. Source: Grill’d website


Business Understanding

Grill’d are a high-end hamburger restaurant that specialise in healthy burgers. Having opened their first store in Victoria in 2004, by February 2021 they have grown to 147 stores across Australia. Their growth strategy has been based on selling franchises, and they use independent property consultants to pick locations.

There are two complementary challenges about picking a location. First, how do you find a location where people will want to eat your burgers. And second, how do you make sure your new location won’t cannibalise sales from your existing stores?

It is the second problem that we will tackle today. We’ll color-code all of Sydney based on how far away any given point is from the nearest existing store. That way, head office will know which locations to rule out because they’re too close to existing stores, and which locations they can investigate further.

Data Understanding

First, we need to understand where the existing restaurants are.

I scraped Sydney restaurant locations from the Grill’d website, and then geocoded it, which involves asking Google Cloud to figure out the latitude and longitude for the locations provided. The script can be found here.

Here’s a table of the Sydney restaurants.

There are 19 stores in Sydney. Let’s locate them on a map.

From a quick scan, we can see that most restaurants are located around the CBD, although there are a few others scattered across the suburbs.

Measuring Distances

To figure out where to place the next store so it won’t get in the way of the existing stores, we want to measure the distance from the existing stores to various other points on the map. So we’re going to need to do two things:

  1. Cut Our Map Into Squares - If we do that, we can measure the distance from the middle of each square to each of our restaurants.

  2. Use The Right Co-ordinate Reference System - When we’re trying to measure the distance between two points in an area as large as Sydney, our measurements get thrown off because of the curvature of the Earth. Geographers get around this by using maths to flatten the map at that location, in the same way that you might flatten a basketball to measure the distance between two points with a ruler.

Below is a simple outline of Greater Sydney. Let’s cut it into small squares of 2km on each side:

On the left you can see the map of Sydney, and on the right is the map after we cut it into 2km squares. Now we’ll be able to measure the distance from the centre of each grid square to the nearest restaurant.

For example, let’s take one random grid square, say, in the middle of the Blue Mountains …

… and now let’s add our 19 restaurants …

… now we can measure the distance between our random grid square and each restaurant. This will allow us to find the nearest one.

Once we understand how to find the nearest restaurant to each grid square, we can apply this to every grid square. This means we can have a map where different colours show distances.

Conclusion

The map can show you at a glance which suburbs are missing out on the deliciousness Grill’d’s Healthy Burgers. Liverpool for instance. And Bankstown. And Hornsby. In principle, we don’t want to open another store in Chatswood as it would cannibalise customers from the store already there.

And now we’ve ruled out areas we shouldn’t open a new store, the question is which suburb should we open our new store. For that we’ll need to find clusters of people with similar demographics and psychographics as Grill’d’s existing customer base. We can do that using data sources like the ABS census, or other third party data providers.

But that’s a blog post for another day.